Benchmarks for pitch types

This one is about the numbers. No pitcher to profile, no story to tell. Instead I’m sharing the initial output of a fairly extensive project—2010 pitch classifications.

I’ve managed to tag and review every pitch thrown so far in 2010, including spring training. The numbers below include only regular season games and, despite my best efforts, there are errors in the pitch classifications. Given the post hoc nature (as opposed to Gameday’s real time) of this labels, and the mix of automate, psuedo-automated and manual processes, I’m fairly confident in the utility of the data set for at least one purpose—creating a baseline for a variety of metrics that can be referred to from here on out.

In other words, when it comes down to an individual pitcher, pitch tags can be moved around but, as a group, there is enough of a sample to use the following numbers as benchmarks.

Here’s your big baseline, a lump of every classified pitch. Unclassified pitches are the result of PITCHf/x glitches (rare) or mid-plate appearance pitching changes (less rare).

rvERA is not league adjusted, park adjusted or starter/reliever adjusted. Batted ball outcomes are regressed towards MLB average outcomes. It’s a toy, maybe a fancy one, but a toy nonetheless.

Type

#

rvERA

MPH

Swing

Whiff

Foul

B:CS

IWZ

Chase

Watch

nkSLG

GB%

LD%

FB%

PU%

HR/FL%

All

131633

4.34

88

0.435

0.211

0.377

2.1

0.511

0.261

0.394

0.516

44%

20%

28%

7.4%

7.5%

These numbers include only 2010, so there are some weather-related changes to come. For example, fastballs (see below) will get faster and more fly balls will leave the park.

You’ll notice, despite a generous strike zone, pitchers have trouble throwing strikes, and the average ground ball rate is 44 percent. Both the GB percentage and whiff rate are up from 2009, so some decline could be coming over the next several hundred thousand pitches.

Now, for each pitch type. You’ll have to pardon my sometimes confusing two-letter abbreviations—please refer to this key.

Pitch-type abbreviations:

CH = Change-ups, may include some splitters that tail more than tumble
CU = Curveballs, probably some slurves
F2 = Two-seam fastball, sinkers, tailing fastballs
F4 = Four-seam fastball, generic fastballs
FC = Cutters and some slutters, can be a fuzzy group
FS = Splitters, foshes and forkballs, may include some other tumbling change-ups
KN = Knuckleballs, although some of Eddie Bonine's (et al.) are not in here
SB = Screwball, sole property of Danny Herrera
SL = Slider or slurve, even some slutters

Type

#

rvERA

MPH

Swing

Whiff

Foul

B:CS

IWZ

Chase

Watch

nkSLG

GB%

LD%

FB%

PU%

HR/FL%

SL

18722

3.82

84

0.456

0.327

0.317

2.4

0.484

0.304

0.374

0.505

45%

18%

29%

8.4%

7.8%

FS

1841

3.87

84

0.501

0.345

0.298

4.0

0.434

0.340

0.280

0.424

48%

19%

25%

7.3%

6.8%

CH

13325

4.16

83

0.491

0.307

0.291

3.7

0.441

0.325

0.287

0.452

50%

18%

25%

7.1%

6.9%

FC

7230

4.22

87

0.475

0.212

0.389

2.3

0.526

0.275

0.337

0.494

44%

21%

26%

9.3%

6.1%

CU

11156

4.47

77

0.373

0.261

0.327

2.2

0.467

0.254

0.487

0.512

49%

19%

27%

4.8%

8.3%

F4

46115

4.49

92

0.421

0.164

0.438

1.7

0.561

0.226

0.416

0.567

35%

21%

34%

9.6%

7.7%

F2

29551

4.53

91

0.430

0.128

0.391

1.9

0.543

0.239

0.400

0.499

52%

20%

23%

4.5%

7.3%

KN

821

4.88

69

0.445

0.227

0.384

2.7

0.515

0.236

0.348

0.563

37%

20%

32%

10.6%

8.1%

SB

42

5.26

66

0.333

0.071

0.286

2.5

0.429

0.250

0.556

0.111

44%

33%

11%

11.1%

0.0%

If a pitcher has a higher than expected HR/FL rate, will it regress toward league average or toward league average by pitch? For example, a fastball/curveball pitcher could be expected to give up more home runs per fly ball-plus-line drive than a sinker/change-up pitcher. If you get that awkward question, “where do ground balls come from?,” you can answer “from sinkers and off-speed pitches.” If your favorite pitcher doesn’t command, or even own, a sinker, a slider or change-up can get the ground ball when needed. You can also look at the above table and understand why a fastball that gets a whiff rate north of .3 is so darn impressive, while a slider with the same rate may not be.

Now let’s try some pitch types grouped together, but not in mutually exclusive groups. Cutters are in FC/SL and F4/FC—all of them. The CH/FS group is probably the most useful combination due to their similarity and overlap, followed by F4/F2 for the same reason. The rest are sketchy or totally arbitrary (KN/SB).

Type

#

rvERA

MPH

Swing

Whiff

Foul

B:CS

IWZ

Chase

Watch

nkSLG

GB%

LD%

FB%

PU%

HR/FL%

FC/SL

25952

3.93

85

0.461

0.295

0.337

2.4

0.496

0.296

0.364

0.502

45%

19%

28%

8.7%

7.3%

SL/CU

29878

4.06

81

0.425

0.302

0.321

2.3

0.478

0.285

0.416

0.508

46%

18%

28%

7.1%

8.0%

CH/FS

15166

4.12

83

0.492

0.312

0.292

3.7

0.440

0.327

0.286

0.449

50%

18%

25%

7.1%

6.9%

F4/FC

53345

4.45

91

0.428

0.171

0.431

1.8

0.556

0.233

0.405

0.557

36%

21%

33%

9.6%

7.5%

F4/F2

75666

4.51

92

0.425

0.150

0.420

1.8

0.554

0.231

0.410

0.540

42%

21%

30%

7.6%

7.5%

KN/SB

863

4.90

69

0.440

0.219

0.379

2.7

0.511

0.237

0.358

0.541

37%

21%

31%

10.6%

7.7%

While I’ve already called most of these groupings arbitrary and sketchy, there is utility hidden in a few places. For example, the SL/CU group may be handy for “breaking pitches” of unknown variety. I’m sure creative minds can think of more uses, and more sophisticated approaches. I hope we’ll see some of that in the comments. If nothing else, I hope this provides a handy reference.

References & ResourcesPITCHf/x data from Sportvision and MLBAM. Pitch classifications by the author.

Comments

Harry, good stuff. A couple of points. Why not have a “wide” and a “tight” strike zone, and then anything chased wide is Chase and anything watched tight is Watched. The borderline pitches shouldn’t be in either chase or watch.

Instead of SLGCON, why not wOBA CON? SLG totally messes the weighting, while wOBA sets it right. And, you can use simpler weights like 0.9 for 1B, 1.3 for 2B, 3B, and 2.0 for HR if you like.

Thanks Tom.
To the first point, at Complete Game Consulting we are working on a improved strike zone model, but it isn’t ready for use in publications yet. I actually pulled the data out at the last minute. I think the notion of tossing the close pitches from chase/watch is fantastic, I’m taking that!

Re SLGCON, I agree, in part. I present linear weight based rvERA already, so I go with something more “traditional” for the rest. But I think showing the non-swing, swing, and in-play+HR weights would be informative as you’re pointed out.

Looking at rvERA it implies pitchers should throw a lot more sliders, cutters and changeups and a lot fewer fastballs. But aren’t those results a function of when the different types of pitches are most likely to be thrown (i.e., the first three when ahead in the count, fastballs when even or behind)? To judge the effectiveness of each pitch type, don’t you need to put it in context of the ball/strike count?

Harry – Are Start_Speeds really calculated at 55 feet when all other start parameters are calculated at y0 or 50 feet? Why would you say that Watch is the inverse of Swing Rate when that is neither mathematically nor conceptually correct?

Otherwise I like the table and commend you on the hard work that you have put in thus far. As far as SLGCON goes I am not sure why Tom would say that it “totally messes the weighting.” Slugging (bases/AB) seems entirely appropriate for a measure “on contact”. I do like his idea of separating the pitches at the edge of the strike zone from those that are either clearly in or clearly out however.

Yes, Tom, Linear Weights better measures the run value of an AB because it includes the value of preserving future PAs by avoiding outs. I understand that as well as you do. But slugging, by measuring just the number of bases per AB, gives a better measure of how hard the ball was hit, which I think is a legitimate measure of the effectiveness of a specific type of pitch.

If you want to throw SLG “out the window and into the sewer” go ahead, but SLG has its uses as a baseball metric. It is just a matter of using it appropriately and I think Harry has.

Peter – MLBAM publishes the data from 50 ft, you can extrapolate location anywhere (home plate, release point) from there. Watch is the inverse of Swing rate in the zone, 1-Watch=Swings in the zone

Harry – Now you are really confusing me. Are you saying that MLBAM is publishing the Pitch f/x Start_Speed as of 50 feet, but that you have extrapolated it back to 55 feet?

I understand that 1-Watch = Swings in the zone. But you don’t give Swings in the zone. You defined Swing as swings/pitches and then said Watch was the inverse of that. So is Swing = swings/all pitches or just swings/pitches in the zone?

Peter, certainly, if you want to measure how “hard a ball is hit”, that’s perfectly legitimate. I question that SLG is that method. I see no reason to accept that 0,1,2,3,4 as the weights for out,1b,2b,3b,hr as a proxy for hardness of ball hit.

One can easily argue that the weights could be:
0.5 contact out
1.0 single
3.0 double
5.0 triple
10.0 HR

Indeed, all you need to do is run a regression of speed off bat to the outcome events, and you can come up a decent regression equation to give you what you want.

That said, based on the great feedback here, I’ll be changing those in the near future. I’ll also be sure to keep the explanations clear.

Re. the 50 ft/55 ft. The data was originally provided at 40 ft, then 55, then they settled on 50. Everything is a parabolic curve fitting process, always has been. Plate locations are calculated, not measured, for example. You may recall Rand talking about the foam board experiments at the first SV Summit.

That slider may be effective because it is set-up by “ineffective” fastballs or it may be effective because it isn’t seen often. This can be teased out, so there’s no need to assume one way or the other. Study the pitcher, how he uses the pitch etc, before determining a change in pitch mix. Or target a specific count/situation where there may be more clarity.

If you multiply Swing by Whiff, that gives you overall Miss%, which should be similar to fangraphs’ SwStr%. Their league average is 8.3%, but here it’s 9.2% – can I assume that the difference is based on how you handle pitchouts (excluded?) and foul tips?

Harry – I am aware of the history of Pitch f/x and the changes in calculating Start_Speed. But I was under the impression that Sportvision had standardized on 50 feet during 2009 and had used that distance for all the calculations during 2010. I was trying to determine whether your definition above:

MPH = speed at release, 55 ft. from the back end of home plate

was just a typographical error and you were using the MPH reported by MLBAM, or if you were using an MPH at 55 feet that you had extrapolated yourself.